Machine Learning | 基于逻辑回归做二分类进行癌症预测

import pandas as pd
import numpy as np
from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression, SGDRegressor,  Ridge, LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error, classification_report
from sklearn.externals import joblib

column = ['Sample code number','Clump Thickness', 'Uniformity of Cell Size','Uniformity of Cell Shape','Marginal Adhesion', 'Single Epithelial Cell Size','Bare Nuclei','Bland Chromatin','Normal Nucleoli','Mitoses','Class']

data = pd.read_csv("breast-cancer-wisconsin.csv", names=column)
data.head()

data = data.replace(to_replace='?', value=np.nan)
data = data.dropna()

x_train, x_test, y_train, y_test = train_test_split(data[column[1:10]], data[column[10]], test_size=0.25)

std = StandardScaler()
x_train = std.fit_transform(x_train)
x_test = std.transform(x_test)

lg = LogisticRegression(C=1.0)
lg.fit(x_train, y_train)
print(lg.coef_)

LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='warn',
          n_jobs=None, penalty='l2', random_state=None, solver='warn',
          tol=0.0001, verbose=0, warm_start=False)LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='warn',
          n_jobs=None, penalty='l2', random_state=None, solver='warn',
          tol=0.0001, verbose=0, warm_start=False)

[[ 1.60392495 -0.11066665  0.93702846  1.01160157 -0.31111269  1.20876603
   1.20701977  1.04581779  0.81269039]]

y_predict = lg.predict(x_test)
print("准确率：", lg.score(x_test, y_test))
print("召回率：", classification_report(y_test, y_predict, labels=[2, 4], target_names=["良性", "恶性"]))